Yazar "Halkaci, H. Selcuk" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Effects of sheet thickness and anisotropy on forming limit curves of AA2024-T4
    (Springer London Ltd, 2013) Dilmec, Murat; Halkaci, H. Selcuk; Ozturk, Fahrettin; Livatyali, Haydar; Yigit, Osman
    In this study, the effects of sheet thickness and anisotropy of AA2024-T4 on forming limit curve (FLC) are experimentally investigated according to ISO 12004-2 standard. A new limit strain measurement method is proposed by using the grid analysis method so as to determine limit strains conveniently and reliably. In addition to the regular test specimens, various widths are added to enhance the FLC's accuracy at the plane strain condition (PSC). The accuracy and reliability of the proposed method are verified for different materials. Results illustrate that an increase in the sheet thickness increases the FLC level. The additional experiments for additional widths improve the accuracy of the FLC at the PSC, and the position of the lowest major strain value differs from the literature. However, the effect of anisotropy on the FLC is found to be insignificant. Finally, experimental and numerical case studies are carried out for conventional deep drawing, stretch drawing, and hydraulic bulge processes. Results reveal that different FLCs are necessary for different thicknesses for accurate predictions.
  • Küçük Resim Yok
    Öğe
    Investigation of the effect of hydromechanical deep drawing process parameters on formability of AA5754 sheets metals by using neuro-fuzzy forecasting approach
    (Ios Press, 2015) Tinkir, Mustafa; Dilmec, Murat; Turkoz, Mevlut; Halkaci, H. Selcuk
    Adaptive neural-network based fuzzy logic inference system (ANFIS) is a useful method instead of costly Finite Element Analysis (FEA) in order to reduce investigation cost of forming processes. In this research, the effect of hydromechanical deep drawing (HDD) process parameters on AA5754-O sheet was investigated by FE simulations with analysis of variance (ANOVA) and Adaptive Neuro-Fuzzy Modeling approach. In order to determine the prediction error of the ANFIS model according to FEA, firstly a series of FEA of the HDD process were conducted according to Taguchi's Design of Experiment Method (DOE). The results of the FEA were confirmed by comparing the thickness distributions of the formed cups by experimentally and numerically. Moreover an adaptive neural-network based fuzzy logic inference system (ANFIS) was created according to results of simulation to predict the maximum thinning of AA5754-O sheet without needing FE simulations. The calculation performances of the ANFIS model were determined by comparing the estimated results with the results of the FE simulations. By using the results of the FE simulations which were conducted according to a matrix plan, the effects of the parameters to the thinning of the blank were determined by the analysis of variance (ANOVA) method. ABAQUS and MATLAB/ANFIS/Simulink softwares were used to realize and simulate proposed techniques. Mean error of prediction result of ANFIS is found as 0.89% according to FEA.
  • Küçük Resim Yok
    Öğe
    Investigation on the Optimal Geometrical Parameters for Cylindrical Cups in Warm Hydromechanical Deep Drawing Process
    (IEEE, 2017) Turkoz, Mevlut; Acar, Dogan; Dilmec, Murat; Halkaci, H. Selcuk
    Warm sheet hydroforming process has being investigated in recent years with its high formability feature. Warm hydromechanical deep drawing (WHDD) which is a type of the warm sheet hydroforming process is important for deeper parts. To determine the formability of the WHDD process the most convenient tool is Limiting Drawing Ratio (LDR). But determination of the LDR accurately needs using of the most convenient tool dimensions. In this study optimal geometrical parameters were investigated by FE Analyses of the process. Consequently optimum punch and die diameters and optimum punch nose and die entrance radiuses were determined for accurate LDR value.